California Covid-19 Analytics

library(readr)  
library(dplyr)  
library(ggplot2)

Here we are doing the simple task of loading we need for plotting and getting our dataset to work and analyize.

statewide_covid_19_hospital_county_data <- read_csv("~/Desktop/covid-19-hospital-data/statewide-covid-19-hospital-county-data.csv")

Here we are loading the directoory of our actual dataset.

summary(statewide_covid_19_hospital_county_data)

##     county          todays_date        hospitalized_covid_confirmed_patients
##  Length:70927       Length:70927       Min.   :   0.00                      
##  Class :character   Class :character   1st Qu.:   1.00                      
##  Mode  :character   Mode  :character   Median :  11.00                      
##                                        Mean   :  73.34                      
##                                        3rd Qu.:  52.00                      
##                                        Max.   :8098.00                      
##                                        NA's   :8                            
##  hospitalized_suspected_covid_patients hospitalized_covid_patients
##  Min.   :-241.000                      Min.   :   0.00            
##  1st Qu.:   0.000                      1st Qu.:   1.00            
##  Median :   0.000                      Median :  12.00            
##  Mean   :   9.492                      Mean   :  82.62            
##  3rd Qu.:   8.000                      3rd Qu.:  63.00            
##  Max.   :1358.000                      Max.   :8422.00            
##  NA's   :9                             NA's   :1267               
##  all_hospital_beds icu_covid_confirmed_patients icu_suspected_covid_patients
##  Min.   :    0     Min.   :   0.00              Min.   : -2.000             
##  1st Qu.:   55     1st Qu.:   0.00              1st Qu.:  0.000             
##  Median :  278     Median :   2.00              Median :  0.000             
##  Mean   : 1181     Mean   :  16.02              Mean   :  1.444             
##  3rd Qu.: 1023     3rd Qu.:  10.00              3rd Qu.:  1.000             
##  Max.   :54966     Max.   :1731.00              Max.   :262.000             
##  NA's   :3602      NA's   :30                   NA's   :7235                
##  icu_available_beds
##  Min.   :-110.00   
##  1st Qu.:   2.00   
##  Median :   8.00   
##  Mean   :  41.21   
##  3rd Qu.:  29.00   
##  Max.   :1502.00   
##  NA's   :802

’)

# Check for missing values
missing_values <- any(is.na(statewide_covid_19_hospital_county_data))

# If there are missing values, handle them appropriately
clean_data <- na.omit(statewide_covid_19_hospital_county_data)

# Store the information 
result_text <- paste("Any missing values in the dataset:", missing_values)

’)

library(dplyr)
county_summary <- clean_data %>%
                   group_by(county) %>%
                   summarize(
                     avg_hospitalized_confirmed = mean(hospitalized_covid_confirmed_patients),
                     max_icu_suspected = max(icu_suspected_covid_patients),
                     total_hospital_beds = sum(all_hospital_beds)
                   )

This code helps us cover crucial summary metrics for each county in our dataset. By grouping the data based on counties, it allows us to compute key indicators like the average count of hospitalized COVID-19 confirmed patients, the maximum count of suspected COVID-19 patients in the ICU, and the total available hospital beds. These statistics offer valuable insights into how hospital resources are utilized across different regions concerning COVID-19 hospitalizations

This code utilizes ggplot2 to create a bar plot visualizing the average count of hospitalized confirmed COVID-19 patients across different counties in California.This visualization is beneficial for our analysis as it provides a clear comparison of the average count of hospitalized confirmed patients across various counties.

library(ggplot2)
ggplot(county_summary, aes(x = county, y = avg_hospitalized_confirmed)) +
  geom_bar(stat = "identity", fill = "skyblue") +
  labs(title = "Average Hospitalized Confirmed Patients by County")

This code creates a visual representation of the average count of confirmed COVID-19 patients requiring hospitalization across different counties. It uses bars to display this information, with each bar representing a county. The height of each bar corresponds to the average count of hospitalized confirmed patients in that particular county. This graph helps us easily compare the average patient count between different areas, allowing us to identify variations in COVID-19 hospitalizations across counties.

Now that we’ve cleaned our data and successful got an accurate depiction of Covid cases per County, we’re going to want to includeboth hospitalized patients and ICU bed availability to improve our variability and get better understanding of how COVID-19 cases are affecting CA as a whole.

In this 3D scatter plot, we visualize confirmed COVID-19 cases, suspected cases, and hospitalized patients as the x, y, and z-axes, respectively. However, the plot exhibits a broad distribution of data, making it challenging to discern distinct patterns. When cluster points in a 3D scatter plot seem disorganized or scattered in multiple directions, it could indicate disparate scales or ranges among the variables.

This dispersion might hinder a clear interpretation of relationships between the variables. To enhance clarity, considering normalizing the variables to ensure consistent scales across axes.

The normalized 3D scatter plot seems to identify a positive correlation between the number of hospitalized COVID-19 confirmed patients and the total hospitalized COVID-19 patients. The clearer and less cluttered visualization allows for a more apparent observation of this relationship.

Positive Correlation: As the count of hospitalized COVID-19 confirmed patients increases, there appears to be a simultaneous increase in the count of total hospitalized COVID-19 patients.

This observation aligns with the trend showcased in the normalized 3D scatter plot, indicating that as the count of confirmed cases rises, there’s a proportional increase in the number of individuals requiring hospitalization due to COVID-19.

This simple ratio plot provides a simple representation of the ratio between hospitalized confirmed patients and available hospital beds

This plot here looks very similar, but it offers a more nuanced view by incorporating multiple factors (hospitalized patients and ICU bed availability) in a weighted index, potentially providing a more comprehensive insight into the situation